2024-03-28T18:59:39Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1343622024-03-13T09:52:57Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243
Gestión del Repositorio Documental de la Universidad de Salamanca
author
Corchado Rodríguez, Emilio Santiago
author
Baruque, Bruno
2017-09-05T11:01:30Z
2017-09-05T11:01:30Z
2012
Neurocomputing. Volumen 75 (1), pp. 171-184. Elsevier BV.
0925-2312 (Print)
http://hdl.handle.net/10366/134362
This study presents a novel version of the Visualization Induced Self-Organizing Map based on the application of a new fusion algorithm for summarizing the results of an ensemble of topology-preserving mapping models. The algorithm is referred to as Weighted Voting Superposition (WeVoS). Its main feature is the preservation of the topology of the map, in order to obtain the most accurate possible visualization of the data sets under study. To do so, a weighted voting process between the units of the maps in the ensemble takes place, in order to determine the characteristics of the units of the resulting map. Several different quality measures are applied to this novel neural architecture known as WeVoS-ViSOM and the results are analyzed, so as to present a thorough study of its capabilities. To complete the study, it has also been compared with the well-know SOM and its fusion version, with the WeVoS-SOM and with two other previously devised fusion Fusion by Euclidean Distance and Fusion by Voronoi Polygon Similarity—based on the analysis of the same quality measures in order to present a complete analysis of its capabilities. All three summarization methods were applied to three widely used data sets from the UCI Repository. A rigorous performance analysis clearly demonstrates that the novel fusion algorithm outperforms the other single and summarization methods in terms of data sets visualization.
en
Attribution-NonCommercial-NoDerivs 3.0 Unported
Computer Science
WeVoS-ViSOM: An ensemble summarization algorithm for enhanced data visualization
info:eu-repo/semantics/article
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
URL
https://gredos.usal.es/bitstream/10366/134362/1/20.wevos-visom-_an_ensemble_summarization_algorithm_for_enhanced_data_visualization.pdf
File
MD5
18cc5320e77a57165451ba607169758e
3304804
application/pdf
20.wevos-visom-_an_ensemble_summarization_algorithm_for_enhanced_data_visualization.pdf
URL
https://gredos.usal.es/bitstream/10366/134362/4/20.wevos-visom-_an_ensemble_summarization_algorithm_for_enhanced_data_visualization.pdf.txt
File
MD5
30ed04d1cc561f4452ed6f2a7b07d38f
64318
text/plain
20.wevos-visom-_an_ensemble_summarization_algorithm_for_enhanced_data_visualization.pdf.txt